Game-aware prediction and interpretation of heart rate dynamics in contact sports using contextually tuned linear time-invariant models

Game-aware prediction and interpretation of heart rate dynamics in contact sports using contextually tuned linear time-invariant models | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Game-aware prediction and interpretation of heart rate dynamics in contact sports using contextually tuned linear time-invariant models Abdullah Zafar, Samuel Guay, Sophie-Andrée Vinet, Jaden Thomas Pantazis, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7247668/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Purpose: Predicting in-game heart rate (HR) in dynamic team sports remains challenging due to limitations of models developed under controlled, laboratory-based conditions. We address this by developing a context-aware framework that forecasts HR dynamics using linear time-invariant (LTI) models, and examine whether these context-sensitive parameters serve as interpretable markers of athlete-or game-level stressors. Methods: We used HR data from 72 university football players over two seasons. First-order LTI systems, with steady-state gain (SSG) and time constant (τ), modeled this data. A gradient boosting regressor predicted SSG and τ for upcoming quarters, using workload, head impact, and BMI as inputs. Model performance was assessed via R2, RMSE, and MAE, compared to population average LTI models. Associations of SSG and τ parameters with athlete- or game-level stressors were also analyzed. Results: The dynamically tuned LTI model significantly outperformed the fixed-parameter baseline in HR prediction accuracy. Furthermore, predicted LTI parameters demonstrated meaningful associations with exertion levels, head impact exposure, and athlete BMI. Conclusion: Context-aware LTI modeling offers both accurate HR forecasting and interpretable physiological insights during competition. This approach lays the groundwork for real-time athlete monitoring and autonomous decision- support systems in high-performance sports environments. systems theory machine learning heart rate physiology team sports Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editor invited by journal 07 Oct, 2025 Reviewers invited by journal 10 Aug, 2025 Editor assigned by journal 04 Aug, 2025 Submission checks completed at journal 01 Aug, 2025 First submitted to journal 01 Aug, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7247668","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":534652393,"identity":"33edfeb7-1752-4278-8d4b-69f438447142","order_by":0,"name":"Abdullah 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02:38:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7247668/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7247668/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":94445929,"identity":"6870488b-67cd-46e5-9695-c571ac8927b5","added_by":"auto","created_at":"2025-10-27 14:33:46","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":649844,"visible":true,"origin":"","legend":"","description":"","filename":"PredictiveHRmodeling.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7247668/v1_covered_07ebb67d-afd1-436d-88e0-8faed8354d74.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Game-aware prediction and interpretation of heart rate dynamics in contact sports using contextually tuned linear time-invariant 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We address this by developing a context-aware framework that forecasts HR dynamics using linear time-invariant (LTI) models, and examine whether these context-sensitive parameters serve as interpretable markers of athlete-or game-level stressors.\u003c/p\u003e\n\u003cp\u003eMethods: We used HR data from 72 university football players over two seasons. First-order LTI systems, with steady-state gain (SSG) and time constant (τ), modeled this data. A gradient boosting regressor predicted SSG and τ for upcoming quarters, using workload, head impact, and BMI as inputs. Model performance was assessed via R2, RMSE, and MAE, compared to population average LTI models. Associations of SSG and τ parameters with athlete- or game-level stressors were also analyzed.\u003c/p\u003e\n\u003cp\u003eResults: The dynamically tuned LTI model significantly outperformed the fixed-parameter baseline in HR prediction accuracy. 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This approach lays the groundwork for real-time athlete monitoring and autonomous decision- support systems in high-performance sports environments.\u003c/p\u003e","manuscriptTitle":"Game-aware prediction and interpretation of heart rate dynamics in contact sports using contextually tuned linear time-invariant models","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-27 11:11:51","doi":"10.21203/rs.3.rs-7247668/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvited","content":"","date":"2025-10-07T11:43:39+00:00","index":"","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-08-10T10:13:24+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-08-04T10:17:35+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-08-01T21:24:02+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Digital Health","date":"2025-08-01T21:21:54+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-digital-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [BMC Digital Health](https://bmcdigitalhealth.biomedcentral.com/)","snPcode":"44247","submissionUrl":"https://submission.nature.com/new-submission/44247/3","title":"BMC Digital Health","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"bcd7765d-2a99-49a3-8385-4a65762240b3","owner":[],"postedDate":"October 27th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-10-27T11:11:51+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-27 11:11:51","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7247668","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7247668","identity":"rs-7247668","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}